Import CSV data

I have a CSV of 2,400 donor records that must join our master contacts table without creating duplicates. Import the file, map email to the contact email field and company to Organization, skip rows missing email, run the de-dup merge rules so existing contacts with the same email are matched, and then flag imported rows with Imported on 2026-07-20 as the source tag. Do a quick spot-check on five random matches and report any ambiguous merges before finalizing.

Before I bring this donor CSV into our contacts table, make the import easy to verify: surface a summary line showing how many rows lack email, show the top three companies and top three email domains, and highlight any rows whose names exactly match existing contacts but have different emails. Also create a temporary Imported Preview view filtered to New Import so a reviewer can scan the risky rows fast.

I tried to import 2,400 donor rows and the preview flagged 120 rows where the full name matches an existing contact but the email differs. I am worried about creating duplicates or overwriting the wrong record. I cannot tell whether the name match means a legitimate update or a different person, and I do not have time to verify each. What’s the most defensible merge rule and the fastest verification step I should run now?

Every monthly CSV import creates name/email mismatches that cost two people a day to clean. I keep using manual name matching and then repairing duplicates. What one habit or checklist change will cut the cleanup time in half and keep data accurate going forward?